Suggested Citation:

Mendez C. (2020). Making maps in R: Using the sf and tmap Packages. R Studio/RPubs. Available at https://rpubs.com/quarcs-lab/tutorial-maps-in-r

This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License.

2 Tutorial objectives

  • Load spatial data files into R
  • Join non-spaital data to spatial data files
  • Create simple choropleth maps

3 Orginal data sources

The non-spatial datafile is from:

The spatial (shapefile) is from:

4 Import the data

4.1 Non-spatial data

4.1.1 Explore the data

## Observations: 67
## Variables: 7
## $ id      <int> 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 2…
## $ growth  <dbl> 0.0254, -0.0254, 0.0773, 0.2583, 0.0709, 0.0247, 0.1059, 0.03…
## $ base    <dbl> 6.157, 5.900, 5.896, 5.315, 5.923, 6.198, 6.075, 6.173, 6.150…
## $ T       <dbl> 3.585, 2.896, 2.763, 3.140, 2.785, 4.118, 3.296, 3.163, 2.740…
## $ E       <dbl> 5.216, 3.458, 4.995, 3.478, 4.805, 5.492, 4.852, 5.131, 5.146…
## $ G       <dbl> 6.192, 6.048, 5.950, 5.354, 5.940, 6.200, 6.113, 6.219, 6.155…
## $ Coastal <int> 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0…

4.1.1.1 Definitions of variables

  • growth: growth rate of per capita real income 1987-2001
  • base: lograrithm of the per apita income in the base year 1987
  • T: average terrorism index
  • E: average years of schooling
  • G: real per capita government expenditures in 1987
  • Coastal: dummy variable which takes the value of one if the province is a coastal province.

4.2 Spatial data

4.2.1 Explore the data

## Observations: 67
## Variables: 21
## $ ObjectID   <int> 1946, 1949, 1952, 1957, 2116, 2150, 2186, 2236, 2278, 2371…
## $ NAME       <chr> "Adana", "Adiyaman", "Afyon", "Agri", "Amasya", "Antalya",…
## $ COUNTRY    <chr> "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", "Turkey"…
## $ ISO_CODE   <chr> "TR01", "TR02", "TR03", "TR04", "TR05", "TR07", "TR08", "T…
## $ ISO_CC     <chr> "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR"…
## $ ISO_SUB    <chr> "01", "02", "03", "04", "05", "07", "08", "09", "10", "11"…
## $ ADMINTYPE  <chr> "Province", "Province", "Province", "Province", "Province"…
## $ DISPUTED   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ NOTES      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ AUTONOMOUS <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COUNTRYAFF <chr> "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", "Turkey"…
## $ CONTINENT  <chr> "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "A…
## $ Land_Type  <chr> "Primary land", "Primary land", "Primary land", "Primary l…
## $ Land_Rank  <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…
## $ Shape_Leng <dbl> 8.589, 5.355, 6.698, 6.553, 4.831, 10.889, 4.740, 6.039, 9…
## $ Shape_Area <dbl> 1.6029, 0.8622, 1.4795, 1.1537, 0.6232, 2.1533, 0.7467, 0.…
## $ idnum      <int> 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19…
## $ province   <chr> "Adana", "Adiyaman", "Afyon", "Agri", "Amasya", "Antalya",…
## $ id         <dbl> 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19…
## $ name_esri  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ geometry   <POLYGON [°]> POLYGON ((36.44 38.22, 36.4..., POLYGON ((39.15 38…
  • Check the Coordinate Reference System
## Coordinate Reference System:
##   EPSG: 4326 
##   proj4string: "+proj=longlat +datum=WGS84 +no_defs"

5 Transform the data

No need to transform any data because both datasets share a common variable id

6 Merge the data

6.1 Keep data as sf object

  • Keep the data as sf class, so we will not lose the coodinate system
## Coordinate Reference System:
##   EPSG: 4326 
##   proj4string: "+proj=longlat +datum=WGS84 +no_defs"
## Observations: 67
## Variables: 27
## $ id         <dbl> 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19…
## $ growth     <dbl> 0.0254, -0.0254, 0.0773, 0.2583, 0.0709, 0.0247, 0.1059, 0…
## $ base       <dbl> 6.157, 5.900, 5.896, 5.315, 5.923, 6.198, 6.075, 6.173, 6.…
## $ T          <dbl> 3.585, 2.896, 2.763, 3.140, 2.785, 4.118, 3.296, 3.163, 2.…
## $ E          <dbl> 5.216, 3.458, 4.995, 3.478, 4.805, 5.492, 4.852, 5.131, 5.…
## $ G          <dbl> 6.192, 6.048, 5.950, 5.354, 5.940, 6.200, 6.113, 6.219, 6.…
## $ Coastal    <int> 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0…
## $ ObjectID   <int> 1946, 1949, 1952, 1957, 2116, 2150, 2186, 2236, 2278, 2371…
## $ NAME       <chr> "Adana", "Adiyaman", "Afyon", "Agri", "Amasya", "Antalya",…
## $ COUNTRY    <chr> "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", "Turkey"…
## $ ISO_CODE   <chr> "TR01", "TR02", "TR03", "TR04", "TR05", "TR07", "TR08", "T…
## $ ISO_CC     <chr> "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR", "TR"…
## $ ISO_SUB    <chr> "01", "02", "03", "04", "05", "07", "08", "09", "10", "11"…
## $ ADMINTYPE  <chr> "Province", "Province", "Province", "Province", "Province"…
## $ DISPUTED   <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ NOTES      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ AUTONOMOUS <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COUNTRYAFF <chr> "Turkey", "Turkey", "Turkey", "Turkey", "Turkey", "Turkey"…
## $ CONTINENT  <chr> "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "A…
## $ Land_Type  <chr> "Primary land", "Primary land", "Primary land", "Primary l…
## $ Land_Rank  <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5…
## $ Shape_Leng <dbl> 8.589, 5.355, 6.698, 6.553, 4.831, 10.889, 4.740, 6.039, 9…
## $ Shape_Area <dbl> 1.6029, 0.8622, 1.4795, 1.1537, 0.6232, 2.1533, 0.7467, 0.…
## $ idnum      <int> 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19…
## $ province   <chr> "Adana", "Adiyaman", "Afyon", "Agri", "Amasya", "Antalya",…
## $ name_esri  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ geometry   <POLYGON [°]> POLYGON ((36.44 38.22, 36.4..., POLYGON ((39.15 38…

7 Plot Thematic Maps

7.1 Quick Map

  • A quick map of the Terrorism variable

7.9 Set color intervals

Enter “style =” followed by one of the options below.

  • equal: divides the range of the variable into n parts.
  • pretty: chooses a number of breaks to fit a sequence of equality spaced ‘round’ values.
  • quantile: equal number of cases in each group
  • jenks: looks for natural breaks in the data
  • cat: when the variable is categorical

Change the number of intervals in the color scheme and how the intervals are spaced. Changing the number of intervals n = 7. So, we have 7 shades instead of the default 5.

7.11 Add borders

You can edit the borders of the shapefile with the tm_borders() function which has many arguments. alpha denotes the level of transparency on a scale from 0 to 1 where 0 is completely transparent.

7.14 Interactive map

## tmap mode set to interactive viewing
## tmap mode set to plotting

Documentation

8 Save a new shapefile

10 Datasets

END

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title: "Making maps in R:"
subtitle: "Using the sf and tmap Packages"
author: "Carlos Mendez"
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Suggested Citation: 

> Mendez C. (2020).  Making maps in R: Using the sf and tmap Packages. R Studio/RPubs. Available at <https://rpubs.com/quarcs-lab/tutorial-maps-in-r>

This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License. 
![](License.png)


# Libraries

```{r message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE)

library(tidyverse)  # Modern data science workflow
library(sf)         # Simple features for R
library(tmap)       # Thematic Maps
library(tmaptools)  # Thematic Maps Tools
library(RColorBrewer) # ColorBrewer Palettes
library(leaflet)    # Interactive web maps
library(rgdal)      # Bindings for the Geospatial Data Abstraction Library
library(rgeos)      # Interface to Geometry Engine - Open Source 


# Change the presentation of decimal numbers to 4 and avoid scientific notation
options(prompt="R> ", digits=4, scipen=999)
```

# Tutorial objectives

- Load spatial data files into R 
- Join non-spaital data to spatial data files
- Create simple choropleth maps


# Orginal data sources

The non-spatial datafile is from: 

- Öcal, N., & Yildirim, J. (2010). [Regional effects of terrorism on economic growth in Turkey: A geographically weighted regression approach.](https://journals.sagepub.com/doi/abs/10.1177/0022343310364576) Journal of Peace Research, 47(4), 477-489.

The spatial (shapefile) is from: 

- [Matthew C. Ingram](http://mattingram.net/)


# Import the data


## Non-spatial data

```{r}
dat <-read.csv("DATASET.csv")
```

### Explore the data

```{r}
glimpse(dat) 
```

#### Definitions of variables

- growth: growth rate of per capita real income 1987-2001
- base: lograrithm of the per apita income in the base year 1987
- T: average terrorism index
- E: average years of schooling
- G: real per capita government expenditures in 1987
- Coastal: dummy variable  which takes the value of one if the province is a coastal province.



## Spatial data

```{r}
mapData <- read_sf("MAP.shp")
```

### Explore the data

```{r}
glimpse(mapData)
```

- Check the Coordinate Reference System

```{r}
st_crs(mapData)
```


# Transform the data

No need to transform any data because both datasets share a common variable `id`


# Merge the data


```{r}
dat_map <- inner_join(
  dat,
  mapData,
  by = "id"
)
```

## Keep data as sf object

- Keep the data as sf class, so we will not lose the coodinate system

```{r}
dat_map <- st_as_sf(dat_map)
st_crs(mapData)
```

```{r}
glimpse(dat_map)
```



# Plot Thematic Maps


## Quick Map 

- A quick map of the Terrorism variable

```{r}
qtm(dat_map, fill = "T")
```


## Simple Map

```{r}
tm_shape(dat_map) + tm_fill("T") 
```


## Remove frame

```{r}
tm_shape(dat_map) + 
  tm_fill("T") +
  tm_layout(frame = FALSE)
```


## Set legend outside

```{r}
tm_shape(dat_map) + tm_fill("T") +
  tm_layout(legend.outside = TRUE, frame = FALSE)
```

## Set legend size

```{r}
tm_shape(dat_map) + tm_fill("T") +
  tm_layout(legend.outside = TRUE, frame = FALSE) +
  tm_legend(text.size = 0.3) 
```



## Set color palette

See color reference [here](https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/)

```{r}
tm_shape(dat_map) + 
  tm_fill("T", palette = "Greens") +
  tm_layout(legend.outside = TRUE, frame = FALSE)
```

```{r}
tm_shape(dat_map) + 
  tm_fill("T", palette = "-Greens") +
  tm_layout(legend.outside = TRUE, frame = FALSE)
```


```{r}
tm_shape(dat_map) + 
  tm_fill("T", palette = "viridis") +
  tm_layout(legend.outside = TRUE, frame = FALSE)
```


## Add polygon names

```{r}
tm_shape(dat_map) + 
  tm_fill("T", palette = "viridis") +
  tm_layout(legend.outside = TRUE, frame = FALSE) +
  tm_text("province", size = "Shape_Area",  auto.placement = F, legend.size.show = FALSE) 
```



[Documentation](https://rdrr.io/cran/tmap/man/tm_text.html)

## Add scale bar

```{r}
tm_shape(dat_map) + 
  tm_fill("T", palette = "viridis") +
  tm_layout(legend.outside = TRUE, frame = FALSE) +
  tm_text("province", size = "Shape_Area",  auto.placement = F, legend.size.show = FALSE)  + 
  tm_scale_bar(position = c("RIGHT", "BOTTOM"))
```


## Set color intervals

Enter “style =” followed by one of the options below.

- equal: divides the range of the variable into n parts.
- pretty: chooses a number of breaks to fit a sequence of equality spaced ‘round’ values. 
- quantile: equal number of cases in each group
- jenks: looks for natural breaks in the data
- cat: when the variable is categorical 

```{r}
tm_shape(dat_map) + 
  tm_fill("T",
          style = "quantile",
          palette = "Reds"
    ) +
  tm_layout(
    legend.outside = TRUE,
    frame = FALSE)
```

Change the number of intervals in the color scheme and how the intervals are spaced. Changing the number of intervals n = 7. So, we have 7 shades instead of the default 5.

```{r}
tm_shape(dat_map) + 
  tm_fill("T",
          style = "quantile",
          n = 7,
          palette = "Reds"
    ) +
  tm_layout(
    legend.outside = TRUE,
    frame = FALSE)
```



## Add histogram

```{r}
tm_shape(dat_map) + 
  tm_fill("T",
          style = "quantile",
          n = 5,
          palette = "YlOrBr",
          legend.hist = TRUE
    ) +
  tm_layout(
    legend.outside = TRUE,
    frame = FALSE)
```


## Add borders

You can edit the borders of the shapefile with the tm_borders() function which has many arguments. alpha denotes the level of transparency on a scale from 0 to 1 where 0 is completely transparent.

```{r}
tm_shape(dat_map) + 
  tm_fill("T",
          style = "quantile",
          palette = "Blues"
    ) + 
  tm_borders(alpha=.4) +
  tm_layout(
    legend.outside = TRUE,
    frame = FALSE)
```

## Add compass

```{r}
tm_shape(dat_map) + 
  tm_fill("T",
          style = "quantile",
          palette = "viridis"
    ) + 
  tm_borders(alpha=.4) + 
  tm_compass(type = "8star", 
              position = c("RIGHT", "BOTTOM"),
              show.labels = 2,
              text.size = 0.35) +
  tm_layout(legend.outside = TRUE,
            frame = FALSE)
```

[Documentation](https://www.rdocumentation.org/packages/tmap/versions/2.3-2/topics/tm_compass)


## Edit the layout

```{r}
tm_shape(dat_map) + 
  tm_fill("T",
          palette = "viridis",
          style = "quantile",
          title = "Terrorist Attacks"
          ) + 
tm_borders(alpha=.4) + 
tm_layout(
  legend.text.size = 0.7,
  legend.title.size = 1,
  legend.position = c("right", "bottom"),
  legend.outside = TRUE,
  frame = FALSE
  ) 
```

[Documentation](https://www.rdocumentation.org/packages/tmap/versions/2.3-2/topics/tm_layout)

## Interactive map


```{r}
tmap_mode("view")

tm_shape(dat_map) + 
  tm_fill("T",
          palette = "viridis",
          id="province",
          popup.vars=c("T", "growth", "base", "E", "G")
          ) + 
	tm_legend(outside=TRUE) +
  tm_layout(frame = FALSE) 

tmap_mode("plot")
```


[Documentation](https://rdrr.io/cran/tmap/man/tm_view.html)

# Save a new shapefile

```{r eval=FALSE}
st_write(dat_map, "dat_map.shp")
```



# References

- <https://rdrr.io/cran/tmap/>

- [Tennekes, M., 2018, tmap: Thematic Maps in R, Journal of Statistical Software, 84(6), 1-39](https://doi.org/10.18637/jss.v084.i06)

- <https://marcinstepniak.eu/post/interactive-choropleth-maps-with-r-and-tmap-part-i/>

- [Practical 5: Making maps in R](https://data.cdrc.ac.uk/tutorial/aa5491c9-cbac-4026-97c9-f9168462f4ac/70c4bc61-0475-4806-9240-4ef1fa649a06)

- [Modern Geospatial Data Analysis with R](http://files.zevross.com/workshops/spatial/slides/html/0-deck-list.html)

- <https://tlorusso.github.io/geodata_workshop/tmap_package>

- <https://rstudio-pubs-static.s3.amazonaws.com/229482_b5b182f650434a36ace4eeef2d2bcd83.html#17>

- <https://mran.microsoft.com/snapshot/2017-12-11/web/packages/tmap/vignettes/tmap-nutshell.html>

- [Chapter 8 Solutions](https://geocompr.github.io/geocompkg/articles/solutions08.html)

- [Geocomputation with R website](https://geocompr.github.io/)

- <https://bookdown.org/lexcomber/brunsdoncomber2e/>


# Datasets

- [Non-spatial](https://github.com/quarcs-lab/tutorial-maps-in-r/blob/master/DATASET.zip?raw=true)
- [Spatial](https://github.com/quarcs-lab/tutorial-maps-in-r/blob/master/MAP.zip?raw=true) 
- [Combined](https://github.com/quarcs-lab/tutorial-maps-in-r/blob/master/turkey_admin1_merge3_all.zip?raw=true)

END
